机器学习在癌症患者自我报告结局中的应用研究进展

doi:10.3761/j.issn.1672-9234.2024.08.020

Abstract

Abstract:

In recent years，patient self-reported outcomes in the field of cancer research has gradually deepened，with a large number of patient self-reported outcomes data meet the advantages of machine learning to deal with a large amount of data. Machine learning in the field of self-reported outcomes of cancer patients has been initially applied，and the performance of the relevant machine learning models is good. This paper reviewed the application of machine learning in predicting complications，survival and mortality after anticancer treatment，longitudinal monitoring of health-related quality of life changes in cancer patients，and assisting in cancer treatment decision-making，which can asssist clinical care practice in the areas of symptom prediction，outcome prediction，quality-of-life monitoring，and rational clinical treatment decision-making in cancer patients.

Key words: Machine learning, Cancer, Patient self-reported outcomes

LÜ Jianxia, WANG Aiping. Progress for research and application of machine learning in the patient-reported outcomes of cancer patients[J].Chinese Journal of Nursing Education, 2024, 21(8): 1018-1024.

References 50

[1]	Department of Health and Human Services FDA Center for Drug Evaluation and Research US,Department of Health and Human Services FDA Center for Biologics Evaluation and Research US, Department of Health and Human Services FDA Center for Devices and Radiological Health US. Guidance for industry:patient-reported outcome measures:use in medical product development to support labeling claims:draft guidance[J]. Health Qual Life Outcomes, 2006, 4(1):79.
[2]	Miller D, Steele Gray C, Kuluski K, et al. Patient-centered care and patient-reported measures:let’s look before we leap[J]. Patient, 2015, 8(4):293-299.
[3]	袁长蓉. 患者报告结局测量的理论与实践[M]. 上海: 上海世界图书出版公司, 2023.
[4]	Peter Harrington. 机器学习实战[M]. 李锐,李鹏,曲亚东,等译. 北京: 人民邮电出版社, 2013.
[5]	Greenhalgh J, Long AF, Flynn R. The use of patient reported outcome measures in routine clinical practice:lack of impact or lack of theory?[J]. Soc Sci Med, 2005, 60(4):833-843. doi: 10.1016/j.socscimed.2004.06.022 pmid: 15571900
[6]	Dai W, Feng WH, Zhang YQ, et al. Patient-reported outcome-based symptom management versus usual care after lung cancer surgery:a multicenter randomized controlled trial[J]. J Clin Oncol, 2022, 40(9):988-996.
[7]	Kotronoulas G, Papadopoulou C, MacNicol L, et al. Feasibility and acceptability of the use of patient-reported outcome measures(PROMs) in the delivery of nurse-led supportive care to people with colorectal cancer[J]. Eur J Oncol Nurs, 2017, 29:115-124. doi: S1462-3889(17)30152-7 pmid: 28720258
[8]	Kotronoulas G, O’Brien F, Simpson MF, et al. Feasibility and acceptability of the use of patient-reported outcome measures in the delivery of nurse-led,supportive care to women with cervical cancer[J]. Clin Nurse Spec, 2017, 31(4):E1-E10.
[9]	Shi QL, Mendoza TR, Wang XS, et al. Using a symptom-specific instrument to measure patient-reported daily functioning in patients with cancer[J]. Eur J Cancer, 2016, 67:83-90. doi: S0959-8049(16)32361-9 pmid: 27620946
[10]	Basch E. Patient-reported outcomes-harnessing patients’ voices to improve clinical care[J]. N Engl J Med, 2017, 376(2):105-108.
[11]	Kluetz PG, Papadopoulos EJ, Johnson LL, et al. Focusing on core patient-reported outcomes in cancer clinical trials-respon-se[J]. Clin Cancer Res, 2016, 22(22):5618. doi: 10.1158/1078-0432.CCR-16-2140 pmid: 28151715
[12]	Doward LC, Gnanasakthy A, Baker MG. Patient reported out-comes:looking beyond the label claim[J]. Health Qual Life Outcomes, 2010, 8:89.
[13]	Lavallee DC, Chenok KE, Love RM, et al. Incorporating patient-reported outcomes into health care to engage patients and enhance care[J]. Health Aff(Millwood), 2016, 35(4):575-582.
[14]	Basch E, Deal AM, Dueck AC, et al. Overall survival results of a trial assessing patient-reported outcomes for symptom monitoring during routine cancer treatment[J]. JAMA, 2017, 318(2):197-198. doi: 10.1001/jama.2017.7156 pmid: 28586821
[15]	Deo RC. Machine learning in medicine[J]. Circulation, 2015, 132(20):1920-1930. doi: 10.1161/CIRCULATIONAHA.115.001593 pmid: 26572668
[16]	Alanazi HO, Abdullah AH, Qureshi KN. A critical review for developing accurate and dynamic predictive models using machine learning methods in medicine and health care[J]. J Med Syst, 2017, 41(4):69. doi: 10.1007/s10916-017-0715-6 pmid: 28285459
[17]	周志华. 机器学习[M]. 北京: 清华大学出版社, 2016.
[18]	刘欢, 张颖婷, 袁长蓉. 患者报告结局在癌症人群中的应用和研究进展[J]. 解放军护理杂志, 2017, 34(16):46-52.
[19]	Rossi LA, Melstrom LG, Fong Y, et al. Predicting post-dischar-ge cancer surgery complications via telemonitoring of patient-reported outcomes and patient-generated health data[J]. J Surg Oncol, 2021, 123(5):1345-1352.
[20]	Yang D, Wei X, Hong Q, et al. Patient-reported outcome-based prediction for postdischarge complications after lung surgery[J]. Thorac Cardiovasc Surg, 2023, 71(8):671-679. doi: 10.1055/s-0043-1768224 pmid: 37186190
[21]	Hasannejadasl H, Osong B, Bermejo I, et al. A comparison of machine learning models for predicting urinary incontinence in men with localized prostate cancer[J]. Front Oncol, 2023, 13:1168219.
[22]	Kober KM, Roy R, Dhruva A, et al. Prediction of evening fatigue severity in outpatients receiving chemotherapy:less may be more[J]. Fatigue, 2021, 9(1):14-32.
[23]	Kober KM, Roy R, Conley Y, et al. Prediction of morning fati-gue severity in outpatients receiving chemotherapy:less may still be more[J]. Support Care Cancer, 2023, 31(5):253. doi: 10.1007/s00520-023-07723-5 pmid: 37039882
[24]	Wang YH, van Dijk L, Mohamed ASR, et al. Predicting late symptoms of head and neck cancer treatment using LSTM and patient reported outcomes[J]. Proc Int Database Eng Appl Symp, 2021, 2021:273-279. doi: 10.1145/3472163.3472177 pmid: 35392138
[25]	邓俊, 王均, 高嫦娥, 等. 免疫检查点抑制剂毒性预测生物标志物的研究进展[J]. 实用医学杂志, 2023, 39(20):2561-2565.
[26]	Iivanainen S, Ekstrom J, Virtanen H, et al. Electronic patient-reported outcomes and machine learning in predicting immune-related adverse events of immune checkpoint inhibitor therapies[J]. BMC Med Inform Decis Mak, 2021, 21(1):205.
[27]	Noel CW, Sutradhar R, Gotlib Conn L, et al. Development and validation of a machine learning algorithm predicting emergency department use and unplanned hospitalization in patients with head and neck cancer[J]. JAMA Otolaryngol Head Neck Surg, 2022, 148(8):764-772.
[28]	Sim JA, Kim YA, Kim JH, et al. The major effects of health-related quality of life on 5-year survival prediction among lung cancer survivors:applications of machine learning[J]. Sci Rep, 2020, 10(1):10693.
[29]	Xu HW, Mohamed M, Flannery M, et al. An unsupervised machine learning approach to evaluating the association of symptom clusters with adverse outcomes among older adults with advanced cancer:a secondary analysis of a randomized clinical trial[J]. JAMA Netw Open, 2023, 6(3):e234198.
[30]	Sidey-Gibbons CJ, Sun C, Schneider A, et al. Predicting 180-day mortality for women with ovarian cancer using machine learning and patient-reported outcome data[J]. Sci Rep, 2022, 12(1):21269. doi: 10.1038/s41598-022-22614-1 pmid: 36481644
[31]	Xu C, Subbiah IM, Lu SC, et al. Machine learning models for 180-day mortality prediction of patients with advanced cancer using patient-reported symptom data[J]. Qual Life Res, 2023, 32(3):713-727.
[32]	Parikh RB, Hasler JS, Zhang YC, et al. Development of machine learning algorithms incorporating electronic health record data,patient-reported outcomes,or both to predict mortality for outpatients with cancer[J]. JCO Clin Cancer Inform, 2022, 6:e2200073.
[33]	Deutsch TM, Pfob A, Brusniak K, et al. Machine learning and patient-reported outcomes for longitudinal monitoring of disease progression in metastatic breast cancer:a multicenter,retrospective analysis[J]. Eur J Cancer, 2023, 188:111-121. doi: 10.1016/j.ejca.2023.04.019 pmid: 37229835
[34]	Lian RX, Hsiao V, Hwang J, et al. Predicting health-related quality of life change using natural language processing in thyroid cancer[J]. Intell Based Med, 2023, 7:100097.
[35]	van Dyk K, Ahn J, Zhou XT, et al. Associating persistent self-reported cognitive decline with neurocognitive decline in older breast cancer survivors using machine learning:the Thinking and living with cancer study[J]. J Geriatr Oncol, 2022, 13(8):1132-1140.
[36]	Kourou K, Manikis G, Poikonen-Saksela P, et al. A machine learning-based pipeline for modeling medical,socio-demogra-phic,lifestyle and self-reported psychological traits as predic-tors of mental health outcomes after breast cancer diagnosis:an initial effort to define resilience effects[J]. Comput Biol Med, 2021, 131:104266.
[37]	殷利, 吕俭霞, 彭姗姗, 等. 头颈肿瘤患者治疗决策困境现状及影响因素研究[J]. 护士进修杂志, 2021, 36(21):1932-1936.
[38]	de Silva D, Ranasinghe W, Bandaragoda T, et al. Machine learn-ing to support social media empowered patients in cancer care and cancer treatment decisions[J]. PLoS One, 2018, 13(10):e0205855.
[39]	Pfob A, Mehrara BJ, Nelson JA, et al. Towards patient-centered decision-making in breast cancer surgery:machine learning to predict individual patient-reported outcomes at 1-year follow-up[J]. Ann Surg, 2023, 277(1):e144-e152.
[40]	Pfob A, Mehrara BJ, Nelson JA, et al. Machine learning to predict individual patient-reported outcomes at 2-year follow-up for women undergoing cancer-related mastectomy and breast reconstruction(INSPiRED-001)[J]. Breast, 2021, 60:111-122.
[41]	孙艳玲, 况艺, 袁潇逸, 等. 癌症生存者经济毒性现状及影响因素[J]. 护理学杂志, 2022, 37(15):24-27,31.
[42]	Sidey-Gibbons C, Pfob A, Asaad M, et al. Development of machine learning algorithms for the prediction of financial toxicity in localized breast cancer following surgical treatment[J]. JCO Clin Cancer Inform, 2021, 5:338-347. doi: 10.1200/CCI.20.00088 pmid: 33764816
[43]	Pinheiro LC, McFatrich M, Lucas N, et al. Child and adoles-cent self-report symptom measurement in pediatric oncology research:a systematic literature review[J]. Qual Life Res, 2018, 27(2):291-319. doi: 10.1007/s11136-017-1692-4 pmid: 28879501
[44]	Baggott C, Cooper BA, Marina N, et al. Symptom assessment in pediatric oncology:how should concordance between children’s and parents’ reports be evaluated?[J]. Cancer Nurs, 2014, 37(4):252-262.
[45]	Lu ZH, Sim JA, Wang JX, et al. Natural language processing and machine learning methods to characterize unstructured patient-reported outcomes:validation study[J]. J Med Internet Res, 2021, 23(11):e26777.
[46]	Quon H, Hui X, Cheng Z, et al. Quantitative evaluation of head and neck cancer treatment-related dysphagia in the develop-ment of a personalized treatment deintensification paradigm[J]. Int J Radiat Oncol Biol Phys, 2017, 99(5):1271-1278.
[47]	Chernew ME, Landrum MB. Targeted supplemental data colle-ction-addressing the quality-measurement conundrum[J]. N Engl J Med, 2018, 378(11):979-981.
[48]	周慧, 林颖, 周国进, 等. 《将患者报告结局纳入临床研究的伦理考量:PRO伦理指南》解读[J]. 中国全科医学, 2023, 26(4):395-400.
[49]	邵珂, 葛洪霞, 刘文崇, 等. 应答漂移对癌症患者自我报告结局影响的研究进展[J]. 护理学杂志, 2023, 38(8):111-115.
[50]	王濯, 陈举凤, 张振香, 等. 生态瞬时评估在慢性病患者体力活动中的应用进展[J]. 护理学杂志, 2023, 38(18):126-129.

Progress for research and application of machine learning in the patient-reported outcomes of cancer patients

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